Assessment of metal contamination in dregded sediments using fractionation and Self-Organizing Maps

Arias, R.; Barona, Astrid; Ibarra-Berastegi, Gabriel; Aranguiz, I.; Ηλίας, Α.

doi:10.1016/j.jhazmat.2007.05.048

Cited by 38 publications

(17 citation statements)

References 22 publications

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“…The high values of Cu in the non-residual form may be related to the high affinity of Cu to organic matters, and it could, in fact, easily form complexes with organic matters due to the high stability of organoCu complexes (Li et al, 2001;Ramizer et al, 2005). Several studies have also reported high concentrations of Cu associated with organic matters in the sediment (Fytianos and Lorantou, 2004;Ramizer et al, 2005;Arias et al, 2007;Wong et al, 2007). The high correlation between copper and organic matter (r=0.83) was observed in this study (Table 3).…”

Section: Fig 2: Percentage Of Residual and Non-residual Forms In Laksupporting

confidence: 81%

Non-residual heavy metals in Lake Nasser bed sediments, Egypt.

Yousry¹

2011

Egypt. J. of Aquatic Biolo. and Fish.

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ABSTRACThe Present study with the determination of concentrations of non-residual heavy metals (Iron, Manganese, Lead, Cadmium, Chromium, Copper, Nickel and Zinc) in Lake Nasser bed sediments as compared with total heavy metals contents in order to identify their environmental risk in Lake water. The analysis involves testing of sediments collected from eight sampling sites along riverine and transtion zones of Lake Nasser. Organic matter content for each of the sediment samples was also reported to study the effect of its content on the accumulation of metals in bed sediments. It was found that, the non-residual heavy metal levels (mg kg -1 ) in bed sediments were 0.11±0. 20.4, 19.9, 25.2, 38.6, 57.6, 49.3, 41.1 and 45.8% from the total content of these metals respectively. Fe-Mn oxides and organic matter seem to be the main carrier phases for the non-residual fraction of these metals. The order of non-residual metal concentrations in sediment samples was found to be Fe>Mn>Zn, Ni>Cu>Cr>Pb>Cd. In the present study, sediment quality assessment was done by calculating sediment pollution index and metal pollution index. The results of the assessment showed that, stream sediments from Lake Nasser are categorized into SPI0 natural sediments in all sites, except one site at km 403 which show SPI1 low polluted sediment. Metal pollution index highlights concentration differences between sites. Moreover, by comparing the results with sediment quality guidelines, it can be found that the Cd, Cu, Zn, Pb and Cr levels belong to the concentration below The Effect Range Low (ERL) and the toxicity to the biota in Lake Nasser was rarely occurred. It was also observed that, non-residual heavy metals were found very low compared with the agricultural land use target. This indicates that, Lake Nasser sediment can be used in amendment of agricultural land. The results of this study recommended the use of a pilot project to evaluate the characteristics of sediment for its potential use in agricultural land and to identify how sediments affect the crop yields. Some statistical applications were applied to study the behavior of the metal contents and their correlations in bed sediments.

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Section: Fig 2: Percentage Of Residual and Non-residual Forms In Laksupporting

confidence: 81%

Non-residual heavy metals in Lake Nasser bed sediments, Egypt.

Yousry¹

2011

Egypt. J. of Aquatic Biolo. and Fish.

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“…For the five pollutants analyzed, predictions are made using all the types of NNs [4][5][6][7][8][9][10]. However, it was necessary to analyze the effect that the spatial variability of pollutants' emissions have on the different models used at each location.…”

Section: Resultsmentioning

confidence: 99%

“…The statistical tools employed have been several types of neural networks (NNs). These NNs obtained can be easily incorporated into the air pollution network management activities to obtain hourly forecasts [8,9,10]. The NNs are the core of the BISTAPOF (BIlbao Short-Term Air POllution Forecast) model.…”

Section: Databasementioning

confidence: 99%

Prediction of air pollution levels using neural networks: influence of spatial variability

Ibarra-Berastegi¹,

Ηλίας²,

Barona³

et al. 2008

WIT Transactions on Ecology and the Environment

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This work focuses on the prediction of hourly levels up to 8 hours ahead for five pollutants (SO 2 , CO, NO 2 , NO and O 3 ) and six locations in the area of Bilbao (Spain). To that end, 216 models based on neural networks (NN) have been built. Spatial variability for the five pollutants has been assessed using Principal Components Analysis and different behaviour has been detected for the nonreactive pollutant (SO 2 ) and the rest (CO, NO 2 , NO and O 3 ). This can be explained by the very local effects involved in the photochemical reactions. The inputs used to feed the NN models intended to predict forthcoming levels of these five pollutants, include a baseline based on autocorrelation plus a linear or nonlinear combination of different meteorological and traffic variables. The nature of these combinations is different depending on the sensor thus showing the importance of the spatial variability to build the models. The number of hourly cases, due to gaps in data predictions, can have a possible range from 11% to 38% depending on the sensor. Depending on the pollutant, location and number of hours ahead the prediction is made, different types of models have been selected. The use of these models based on NNs can provide Bilbao's air pollution network originally designed for diagnosis purposes, with short-term, real time forecasting capabilities. The performance of these models at the different sensors in the area range from a maximum value of R 2 =0.88 for the prediction of NO 2 1 hour ahead, to a minimum value of R 2 =0.15 for the prediction of ozone 8 hours ahead. These boundaries and the limitation in which the number of cases that predictions are possible represent the maximum forecasting capability that Bilbao's network can provide in real-life operating conditions.

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“…Thus, in the middle estuary, due to intense dredging operations carried out for navigational purposes, higher lead concentrations were detected in the upper parts of the cores (0-1 m) than in the lower parts (Cearreta et al 2002, Belzunce et al 2004b, Arias et al 2008. However, in the outer and middle reaches, higher lead concentrations were measured both in the upper and lower parts of the cores.…”

Section: Sedimentmentioning

confidence: 94%

“…Different studies of percussion cores from several sites of the Nervión estuary (Cearreta et al 2002, Belzunce et al 2004b, Arias et al, 2008 have distinguished three historical environmental zones related to the industrial activity (Cearreta et al 2002). Thus, in the middle estuary, due to intense dredging operations carried out for navigational purposes, higher lead concentrations were detected in the upper parts of the cores (0-1 m) than in the lower parts (Cearreta et al 2002, Belzunce et al 2004b, Arias et al 2008.…”

Section: Sedimentmentioning

confidence: 99%